38 research outputs found
Mitigation of Attacks via Improved Network Security in IoT Network using Machine Learning
In this paper, we develop a support vector machine (SVM) based attack mitigation technique from the IoT network. The SVM aims to classify the features related to the attacks based on pre-processed and feature extracted information. The simulation is conducted in terms of accuracy, precision, recall and f-measure over KDD datasets. The results show that the proposed SVM classifier obtains high grade of classification accuracy in both training and testing datasets
Maximum power point tracking implementation by Dspace controller integrated through Z-Source inverter using particle swarm optimization technique for photovoltaic applications
Maximum Power Point Tracking (MPPT) technique is used to extract maximum power from the photovoltaic system. This paper involves working on an enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic (PV) system integrated through Z-Source inverter. The main benefit of the proposed method is the diminishing of the steady-state oscillation when the maximum power point (MPP) is located. Additionally, during an extreme environmental condition, such as partial shading and large fluctuations of irradiance and temperature, the proposed method has the capability to track the MPP. This algorithm is implemented in dspace 1104 controller. MATLAB simulations are carried out under varying irradiance and temperature conditions to evaluate its effectiveness. Its performance is compared with a conventional method like Perturb and observe (P&O) method
International Consensus Statement on Rhinology and Allergy: Rhinosinusitis
Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICARâRS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICARâRSâ2021 as well as updates to the original 140 topics. This executive summary consolidates the evidenceâbased findings of the document. Methods: ICARâRS presents over 180 topics in the forms of evidenceâbased reviews with recommendations (EBRRs), evidenceâbased reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICARâRSâ2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidenceâbased management algorithm is provided. Conclusion: This ICARâRSâ2021 executive summary provides a compilation of the evidenceâbased recommendations for medical and surgical treatment of the most common forms of RS
NEURAL NETWORK BASED VECTOR CONTROL OF INDUCTION MOTOR
Stator current drift compensation of induction motor based on RBF neural network is proposed here. In vector control of induction motor decoupling of speed and rotor flux equations and their simultaneous control are used to achieve the highest efficiency and fast dynamic performance. The highest efficiency is reached when the proper flux is selected and as a result of dynamic decoupling of speed and rotor flux equations, the rotor flux can be modified to achieve the highest efficiency and make the speed be at its desired value. The precise control of these changes can also be done using radial basis function neural network (RBFNN). Once neural network gets trained then it is able to differentiate between normal and fault conditions and therefore acts in accordance to the change that could bring back the system to normal condition. Here, neural network is used to compute the appropriate set of voltage and frequency to achieve the maximum efficiency for any value of operating torque and motor speed